# 🚀 Quickstart

Activate the snakemake environment

```
conda activate snakemake
```

Call the pipeline with  `-n` for a dry run and `-q` for reduced output.
Here's the command for running preprocessing, integration and metrics

```
bash run_example.sh preprocessing_all integration_all metrics_all -nq
```

```
Job stats:
job                                    count
-----------------------------------  -------
integration_all                            1
integration_barplot_per_dataset            3
integration_benchmark_per_dataset          1
integration_compute_umap                   6
integration_plot_umap                      6
integration_postprocess                    6
integration_prepare                        1
integration_run_method                     3
preprocessing_assemble                     1
preprocessing_highly_variable_genes        1
preprocessing_normalize                    1
preprocessing_pca                          1
total                                     31

Reasons:
    (check individual jobs above for details)
    input files updated by another job:
        integration_all, integration_barplot_per_dataset, integration_benchmark_per_dataset, integration_compute_umap, integration_plot_umap, integration_postprocess, integration_prepare, integration_run_method, preprocessing_assemble, preprocessing_highly_variable_genes, preprocessing_pca                                                                                             
    missing output files:
        integration_benchmark_per_dataset, integration_compute_umap, integration_postprocess, integration_prepare, integration_run_method, preprocessing_assemble, preprocessing_highly_variable_genes, preprocessing_normalize, preprocessing_pca

This was a dry-run (flag -n). The order of jobs does not reflect the order of execution.
```

If the dryrun was successful, you can let Snakemake compute the different steps of the workflow with e.g. 10 cores:

```
bash run_example.sh preprocessing_all integration_all metrics_all -c 10
```

> You have now successfully called the example pipeline! 🎉  
> Read on to learn how to configure your own workflow.